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Acceleration Component
The Experts below are selected from a list of 189 Experts worldwide ranked by ideXlab platform
Eberhard Bodenschatz – 1st expert on this subject based on the ideXlab platform

Experimental Lagrangian Acceleration probability density function measurement
Physica D: Nonlinear Phenomena, 2020CoAuthors: Nicolas Mordant, A.m. Crawford, Eberhard BodenschatzAbstract:submitted for the special issue of Physica D: “Anomalous Distributions” 11 pages, 6 figures revised version: light modifications of the figures and the textInternational audienceWe report experimental results on the Acceleration Component probability distribution function at $R_\lambda = 690$ to probabilities of less than $10^{7}$. This is an improvement of more than an order of magnitude over past measurements and allows us to conclude that the fourth moment converges and the flatness is approximately 55. We compare our probability distribution to those predicted by several models inspired by nonextensive statistical mechanics. We also look at Acceleration Component probability distributions conditioned on a velocity Component for conditioning velocities as high as 3 times the standard deviation and find them to be highly nonGaussian

Joint statistics of the lagrangian Acceleration and velocity in fully developed turbulence
Physical Review Letters, 2005CoAuthors: Alice M. Crawford, Nicolas Mordant, Eberhard BodenschatzAbstract:We report experimental results on the joint statistics of the Lagrangian Acceleration and velocity in highly turbulent flows. The Acceleration was measured up to a microscale Reynolds number R(lambda)=690 using high speed silicon strip detectors from high energy physics. The Acceleration variance was observed to be strongly dependent on the velocity, following a HeisenbergYaglomlike u(9/2) increase. However, the shape of the probability density functions of the Acceleration Component conditioned on the same Component of the velocity when normalized by the Acceleration variance was observed to be independent of velocity and to coincide with the unconditional probability density function of the Acceleration Components. This observation imposes a strong mathematical constraint on the possible functional form of the Acceleration probability distribution function.

Experimental Lagrangian Acceleration probability density function measurement
Physica D: Nonlinear Phenomena, 2004CoAuthors: Nicolas Mordant, Alice M. Crawford, Eberhard BodenschatzAbstract:Abstract We report experimental results on the Acceleration Component probability distribution function at Rλ=690 to probabilities of less than 10−7. This is an improvement of more than an order of magnitude over past measurements and allows us to conclude that the fourth moment converges and the flatness is approximately 55. We compare our probability distribution to those predicted by several models inspired by nonextensive statistical mechanics. We also look at Acceleration Component probability distributions conditioned on a velocity Component for conditioning velocities as high as three times the standard deviation and find them to be highly nonGaussian.
Mohammad I. Younis – 2nd expert on this subject based on the ideXlab platform

Simple Fall Criteria for MEMS Sensors: Data Analysis and Sensor Concept
Volume 6: 10th International Conference on Multibody Systems Nonlinear Dynamics and Control, 2014CoAuthors: Alwathiqbellah Ibrahim, Mohammad I. YounisAbstract:This paper presents a new and simple fall detection concept based on detailed experimental data of human falling and Activities of Daily Living (ADL). Establishing appropriate fall algorithms compatible with MEMS sensors requires detailed data on falls and ADL that indicate clearly the variations of the kinematics at the possible sensor node location on the human body, such as hip, head, and chest. Currently, there is a lack of data on the exact direction and magnitude of each Acceleration Component associated with these node locations. This is crucial for MEMS structures, which have inertia elements very close to the substrate and are capacitively biased, and hence, are very sensitive to the direction of motion whether it is toward or away from the substrate. This work presents detailed data of the Acceleration Components on various locations on the human body during various kinds of falls and ADL. An algorithm for fall detection based on MEMS switches is then established. A new sensing concept based on the algorithm is proposed. The concept is based on employing several inertia sensors, which are triggered simultaneously, as electrical switches connected in series, upon receiving a true fall signal. In the case of everyday life activities, some or no switches will be triggered resulting in an open circuit configuration, thereby preventing false positive. Lumpedparameter model is presented for the device and preliminary simulation results are presented illustrating the new device concept.Copyright © 2014 by ASME

Simple fall criteria for MEMS sensors: data analysis and sensor concept.
Sensors, 2014CoAuthors: Alwathiqbellah Ibrahim, Mohammad I. YounisAbstract:This paper presents a new and simple fall detection concept based on detailed experimental data of human falling and the activities of daily living (ADLs). Establishing appropriate fall algorithms compatible with MEMS sensors requires detailed data on falls and ADLs that indicate clearly the variations of the kinematics at the possible sensor node location on the human body, such as hip, head, and chest. Currently, there is a lack of data on the exact direction and magnitude of each Acceleration Component associated with these node locations. This is crucial for MEMS structures, which have inertia elements very close to the substrate and are capacitively biased, and hence, are very sensitive to the direction of motion whether it is toward or away from the substrate. This work presents detailed data of the Acceleration Components on various locations on the human body during various kinds of falls and ADLs. A twodegreeoffreedom model is used to help interpret the experimental data. An algorithm for fall detection based on MEMS switches is then established. A new sensing concept based on the algorithm is proposed. The concept is based on employing several inertia sensors, which are triggered simultaneously, as electrical switches connected in series, upon receiving a true fall signal. In the case of everyday life activities, some or no switches will be triggered resulting in an open circuit configuration, thereby preventing false positive. Lumpedparameter model is presented for the device and preliminary simulation results are presented illustrating the new device concept.
Alice M. Crawford – 3rd expert on this subject based on the ideXlab platform

Joint statistics of the lagrangian Acceleration and velocity in fully developed turbulence
Physical Review Letters, 2005CoAuthors: Alice M. Crawford, Nicolas Mordant, Eberhard BodenschatzAbstract:We report experimental results on the joint statistics of the Lagrangian Acceleration and velocity in highly turbulent flows. The Acceleration was measured up to a microscale Reynolds number R(lambda)=690 using high speed silicon strip detectors from high energy physics. The Acceleration variance was observed to be strongly dependent on the velocity, following a HeisenbergYaglomlike u(9/2) increase. However, the shape of the probability density functions of the Acceleration Component conditioned on the same Component of the velocity when normalized by the Acceleration variance was observed to be independent of velocity and to coincide with the unconditional probability density function of the Acceleration Components. This observation imposes a strong mathematical constraint on the possible functional form of the Acceleration probability distribution function.

Experimental Lagrangian Acceleration probability density function measurement
Physica D: Nonlinear Phenomena, 2004CoAuthors: Nicolas Mordant, Alice M. Crawford, Eberhard BodenschatzAbstract:Abstract We report experimental results on the Acceleration Component probability distribution function at Rλ=690 to probabilities of less than 10−7. This is an improvement of more than an order of magnitude over past measurements and allows us to conclude that the fourth moment converges and the flatness is approximately 55. We compare our probability distribution to those predicted by several models inspired by nonextensive statistical mechanics. We also look at Acceleration Component probability distributions conditioned on a velocity Component for conditioning velocities as high as three times the standard deviation and find them to be highly nonGaussian.

Measurement of particle Accelerations in fully developed turbulence
Journal of Fluid Mechanics, 2002CoAuthors: Greg Voth, Alice M. Crawford, A. La Porta, Jim Alexander, Eberhard BodenschatzAbstract:We use silicon strip detectors (originally developed for the CLEO III highenergy particle physics experiment) to measure fluid particle trajectories in turbulence with temporal resolution of up to 70000 frames per second. This high frame rate allows the Kolmogorov time scale of a turbulent water flow to be fully resolved for 140 [ges ] R λ [ges ] 970. Particle trajectories exhibiting Accelerations up to 16000 m s
−2 (40
times the r.m.s. value) are routinely observed. The probability density function of the Acceleration is found to have Reynoldsnumberdependent stretched exponential tails. The moments of the Acceleration distribution are calculated. The scaling of the Acceleration Component variance with the energy dissipation is found to be consistent with the results for lowReynoldsnumber direct numerical simulations, and with the K41based Heisenberg–Yaglom prediction for R λ [ges ] 500. The Acceleration flatness is found to increase with Reynolds number, and to exceed 60 at R λ = 970. The coupling of the Acceleration to the largescale anisotropy is found to be large at low Reynolds number and to decrease as the Reynolds number increases, but to persist at all Reynolds numbers measured. The dependence of the Acceleration variance on the size and density of the tracer particles is measured. The autocorrelation function of an Acceleration Component is measured, and is found to scale with the Kolmogorov time τ η .